def simulation(neuron_count, connectivity, leaking_rate, input_scale,
feedback_scale, washout_time, train_time):
network = esn.create_network(
inputCount=13,
neuronCount=neuron_count,
outputCount=1,
connectivity=connectivity,
leakingRate=leaking_rate,
useOrthonormalMatrix=True)
input_scalings = numpy.ones(13) * input_scale
input_scalings[0] *= 0.03
network.set_input_scalings(input_scalings)
network.set_feedback_scalings([feedback_scale])
trainer = training.Trainer(network, washout_time=washout_time,
train_time=train_time)
stable = True
error = detecting.Error(0.5 / SIM_STEP, 0.3 / SIM_STEP)
try:
while trainer.time < train_time:
trainer.step(SIM_STEP)
while trainer.time < (train_time + FREERUN_TIME):
trainer.step(SIM_STEP)
ref = trainer.outputs.value
out = network.capture_output(1)[0]
error.update(ref, out)
except esn.OutputIsNotFinite:
stable = False
return trainer.time, stable, error.count
def __init__(self):
print("Generating network...")
self.network = esn.create_network(
inputCount=INPUT_COUNT,
neuronCount=NEURON_COUNT,
outputCount=1,
connectivity=CONNECTIVITY,
useOrthonormalMatrix=True)
self.input_audio = input.Audio()
self.server = server.Server(self)
self.server.start()
print("Free running...")
self.state = Daemon.RunningState()
self.output_queue = None
self.input_frames = None
CONNECTIVITY = 0.5
SIM_STEP = 0.01
SLEEP_TIME = 0.02
NORMALIZE_TIME = 50.0
WASHOUT_TIME = 50.0
TRAIN_TIME = 300.0
if __name__ == "__main__":
# Create network
print("Generating network...")
network = esn.create_network(
inputCount=INPUT_COUNT,
neuronCount=NEURON_COUNT,
outputCount=1,
connectivity=CONNECTIVITY,
useOrthonormalMatrix=True
)
# Train the network
trainer = training.Trainer(network, washout_time=WASHOUT_TIME,
train_time=TRAIN_TIME)
# Calculate input scalings
print("Normalizing inputs...")
inmin = numpy.finfo(float).max
inmax = numpy.finfo(float).min
for i in range(int(NORMALIZE_TIME / SIM_STEP)):
inputs = trainer.inputs
